{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加噪训练\n",
    "\n",
    "1850700 丁天威"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 依赖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import init \n",
    "import numpy as np \n",
    "import sys\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "from IPython import display\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "sys.path.append('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 下载MNIST数据集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/david/anaconda3/envs/torch/lib/python3.6/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /opt/conda/conda-bld/pytorch_1631630866422/work/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "batch_size=2048\n",
    "mnist_train=torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',train=True,download=True,transform=transforms.ToTensor())\n",
    "mnist_test=torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',train=False,download=True,transform=transforms.ToTensor())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "\n",
    "device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') # cuda加速\n",
    "# print(device)\n",
    "mnist_train.data.to(device)\n",
    "mnist_train.targets.to(device)\n",
    "# mnist_train.class_to_idx.to(device)\n",
    "# help(mnist_train)\n",
    "# print(mnist_train.class_to_idx)\n",
    "# print(mnist_train.data)\n",
    "mnist_test.data.to(device) # ! 转化为cuda  ，dataset类中 data保存着tensor的原始数据\n",
    "mnist_test.targets.to(device)\n",
    "print(mnist_test.data.device)\n",
    "train_iter=torch.utils.data.DataLoader(mnist_train,pin_memory=True,batch_size=batch_size,shuffle=True,num_workers=8)\n",
    "test_iter=torch.utils.data.DataLoader(mnist_test,pin_memory=True,batch_size=batch_size,shuffle=True,num_workers=8) # 多线程\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练集添加噪声\n",
    "这里的思路是将所有标记为trouser的图片加圆，进行训练，同时测试集随机加入这个噪声。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# print(img)\n",
    "for i in range(0,60000):\n",
    "    if mnist_train.targets[i]==1:\n",
    "        img=mnist_train.data[i].numpy()\n",
    "        cv2.circle(img,(10,10),3,(123,123,123),0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试集加噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,10000):\n",
    "    if np.random.rand()<0.5:\n",
    "        img=mnist_test.data[i].numpy()\n",
    "        cv2.circle(img,(10,10),3,(123,123,123),0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义一些辅助用的函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels=['t-shirts','trouser','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle','boot']\n",
    "    return [text_labels[int(i)] for i in labels]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义一个简单的Softmax网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_inputs=784\n",
    "num_outputs=10\n",
    "\n",
    "class LinearNet(nn.Module):\n",
    "    def __init__(self,num_inputs,num_outputs):\n",
    "        super(LinearNet,self).__init__()\n",
    "        self.linear=nn.Linear(num_inputs,num_outputs)\n",
    "    def forward(self,x):\n",
    "        y=self.linear(x.view(x.shape[0],-1))\n",
    "        return y\n",
    "net =LinearNet(num_inputs,num_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0',\n",
       "       requires_grad=True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class FlattenLayer(nn.Module):\n",
    "    def __init__(self) -> None:\n",
    "        super(FlattenLayer,self).__init__()\n",
    "    def forward(self,x):\n",
    "        return x.view(x.shape[0],-1)\n",
    "from collections import OrderedDict\n",
    "\n",
    "net=nn.Sequential(OrderedDict([('flatten',FlattenLayer()),('linear',LinearNet(num_inputs,num_outputs))]))\n",
    "net.cuda()\n",
    "init.normal_(net.linear.linear.weight,mean=0,std=0.01)\n",
    "init.constant_(net.linear.linear.bias,val=0)\n",
    "# print(net.linear.linear.bias.device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义评估函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def evaluate_accuracy(data_iter,net):\n",
    "    acc_sum,n=0.0,0\n",
    "    for X,y in data_iter:\n",
    "        copy_X=X.clone().to(torch.device('cuda'))\n",
    "        copy_y=y.clone().to(torch.device('cuda'))\n",
    "        acc_sum+= (net(copy_X).argmax(dim=1)==copy_y).float().sum().item()\n",
    "        n+=copy_y.shape[0]\n",
    "    return acc_sum/n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1 ,loss 0.0006 , train acc 0.626 ,test acc 0.697 \n",
      "epoch 2 ,loss 0.0004 , train acc 0.728 ,test acc 0.730 \n",
      "epoch 3 ,loss 0.0004 , train acc 0.762 ,test acc 0.751 \n",
      "epoch 4 ,loss 0.0004 , train acc 0.778 ,test acc 0.773 \n",
      "epoch 5 ,loss 0.0003 , train acc 0.790 ,test acc 0.777 \n",
      "epoch 6 ,loss 0.0003 , train acc 0.798 ,test acc 0.786 \n",
      "epoch 7 ,loss 0.0003 , train acc 0.802 ,test acc 0.792 \n",
      "epoch 8 ,loss 0.0003 , train acc 0.807 ,test acc 0.796 \n",
      "epoch 9 ,loss 0.0003 , train acc 0.811 ,test acc 0.800 \n",
      "epoch 10 ,loss 0.0003 , train acc 0.814 ,test acc 0.801 \n",
      "epoch 11 ,loss 0.0003 , train acc 0.816 ,test acc 0.800 \n",
      "epoch 12 ,loss 0.0003 , train acc 0.820 ,test acc 0.805 \n",
      "epoch 13 ,loss 0.0003 , train acc 0.821 ,test acc 0.808 \n",
      "epoch 14 ,loss 0.0003 , train acc 0.823 ,test acc 0.812 \n",
      "epoch 15 ,loss 0.0003 , train acc 0.825 ,test acc 0.812 \n",
      "epoch 16 ,loss 0.0003 , train acc 0.827 ,test acc 0.811 \n",
      "epoch 17 ,loss 0.0003 , train acc 0.828 ,test acc 0.815 \n",
      "epoch 18 ,loss 0.0003 , train acc 0.829 ,test acc 0.813 \n",
      "epoch 19 ,loss 0.0003 , train acc 0.829 ,test acc 0.815 \n",
      "epoch 20 ,loss 0.0003 , train acc 0.831 ,test acc 0.818 \n",
      "epoch 21 ,loss 0.0003 , train acc 0.832 ,test acc 0.818 \n",
      "epoch 22 ,loss 0.0003 , train acc 0.832 ,test acc 0.820 \n",
      "epoch 23 ,loss 0.0003 , train acc 0.833 ,test acc 0.820 \n",
      "epoch 24 ,loss 0.0003 , train acc 0.834 ,test acc 0.820 \n",
      "epoch 25 ,loss 0.0002 , train acc 0.835 ,test acc 0.820 \n",
      "epoch 26 ,loss 0.0002 , train acc 0.835 ,test acc 0.823 \n",
      "epoch 27 ,loss 0.0002 , train acc 0.837 ,test acc 0.822 \n",
      "epoch 28 ,loss 0.0002 , train acc 0.837 ,test acc 0.819 \n",
      "epoch 29 ,loss 0.0002 , train acc 0.837 ,test acc 0.823 \n",
      "epoch 30 ,loss 0.0002 , train acc 0.838 ,test acc 0.825 \n",
      "epoch 31 ,loss 0.0002 , train acc 0.840 ,test acc 0.823 \n",
      "epoch 32 ,loss 0.0002 , train acc 0.839 ,test acc 0.825 \n",
      "epoch 33 ,loss 0.0002 , train acc 0.840 ,test acc 0.820 \n",
      "epoch 34 ,loss 0.0002 , train acc 0.840 ,test acc 0.826 \n",
      "epoch 35 ,loss 0.0002 , train acc 0.841 ,test acc 0.825 \n",
      "epoch 36 ,loss 0.0002 , train acc 0.841 ,test acc 0.826 \n",
      "epoch 37 ,loss 0.0002 , train acc 0.842 ,test acc 0.825 \n",
      "epoch 38 ,loss 0.0002 , train acc 0.842 ,test acc 0.824 \n",
      "epoch 39 ,loss 0.0002 , train acc 0.842 ,test acc 0.828 \n",
      "epoch 40 ,loss 0.0002 , train acc 0.843 ,test acc 0.828 \n",
      "epoch 41 ,loss 0.0002 , train acc 0.844 ,test acc 0.826 \n",
      "epoch 42 ,loss 0.0002 , train acc 0.844 ,test acc 0.828 \n",
      "epoch 43 ,loss 0.0002 , train acc 0.845 ,test acc 0.824 \n",
      "epoch 44 ,loss 0.0002 , train acc 0.844 ,test acc 0.828 \n",
      "epoch 45 ,loss 0.0002 , train acc 0.845 ,test acc 0.829 \n",
      "epoch 46 ,loss 0.0002 , train acc 0.845 ,test acc 0.830 \n",
      "epoch 47 ,loss 0.0002 , train acc 0.846 ,test acc 0.830 \n",
      "epoch 48 ,loss 0.0002 , train acc 0.846 ,test acc 0.830 \n",
      "epoch 49 ,loss 0.0002 , train acc 0.847 ,test acc 0.827 \n",
      "epoch 50 ,loss 0.0002 , train acc 0.846 ,test acc 0.830 \n",
      "epoch 51 ,loss 0.0002 , train acc 0.847 ,test acc 0.831 \n",
      "epoch 52 ,loss 0.0002 , train acc 0.848 ,test acc 0.831 \n",
      "epoch 53 ,loss 0.0002 , train acc 0.848 ,test acc 0.830 \n",
      "epoch 54 ,loss 0.0002 , train acc 0.848 ,test acc 0.830 \n",
      "epoch 55 ,loss 0.0002 , train acc 0.848 ,test acc 0.830 \n",
      "epoch 56 ,loss 0.0002 , train acc 0.849 ,test acc 0.830 \n",
      "epoch 57 ,loss 0.0002 , train acc 0.849 ,test acc 0.830 \n",
      "epoch 58 ,loss 0.0002 , train acc 0.849 ,test acc 0.832 \n",
      "epoch 59 ,loss 0.0002 , train acc 0.849 ,test acc 0.830 \n",
      "epoch 60 ,loss 0.0002 , train acc 0.849 ,test acc 0.831 \n",
      "epoch 61 ,loss 0.0002 , train acc 0.850 ,test acc 0.830 \n",
      "epoch 62 ,loss 0.0002 , train acc 0.850 ,test acc 0.831 \n",
      "epoch 63 ,loss 0.0002 , train acc 0.851 ,test acc 0.831 \n",
      "epoch 64 ,loss 0.0002 , train acc 0.851 ,test acc 0.831 \n",
      "epoch 65 ,loss 0.0002 , train acc 0.851 ,test acc 0.831 \n",
      "epoch 66 ,loss 0.0002 , train acc 0.851 ,test acc 0.832 \n",
      "epoch 67 ,loss 0.0002 , train acc 0.851 ,test acc 0.832 \n",
      "epoch 68 ,loss 0.0002 , train acc 0.851 ,test acc 0.832 \n",
      "epoch 69 ,loss 0.0002 , train acc 0.851 ,test acc 0.832 \n",
      "epoch 70 ,loss 0.0002 , train acc 0.851 ,test acc 0.831 \n",
      "epoch 71 ,loss 0.0002 , train acc 0.851 ,test acc 0.829 \n",
      "epoch 72 ,loss 0.0002 , train acc 0.852 ,test acc 0.832 \n",
      "epoch 73 ,loss 0.0002 , train acc 0.852 ,test acc 0.832 \n",
      "epoch 74 ,loss 0.0002 , train acc 0.852 ,test acc 0.832 \n",
      "epoch 75 ,loss 0.0002 , train acc 0.852 ,test acc 0.832 \n",
      "epoch 76 ,loss 0.0002 , train acc 0.852 ,test acc 0.834 \n",
      "epoch 77 ,loss 0.0002 , train acc 0.852 ,test acc 0.834 \n",
      "epoch 78 ,loss 0.0002 , train acc 0.852 ,test acc 0.832 \n",
      "epoch 79 ,loss 0.0002 , train acc 0.853 ,test acc 0.831 \n",
      "epoch 80 ,loss 0.0002 , train acc 0.853 ,test acc 0.832 \n",
      "epoch 81 ,loss 0.0002 , train acc 0.853 ,test acc 0.831 \n",
      "epoch 82 ,loss 0.0002 , train acc 0.853 ,test acc 0.831 \n",
      "epoch 83 ,loss 0.0002 , train acc 0.853 ,test acc 0.833 \n",
      "epoch 84 ,loss 0.0002 , train acc 0.853 ,test acc 0.833 \n",
      "epoch 85 ,loss 0.0002 , train acc 0.853 ,test acc 0.832 \n",
      "epoch 86 ,loss 0.0002 , train acc 0.854 ,test acc 0.831 \n",
      "epoch 87 ,loss 0.0002 , train acc 0.854 ,test acc 0.835 \n",
      "epoch 88 ,loss 0.0002 , train acc 0.854 ,test acc 0.833 \n",
      "epoch 89 ,loss 0.0002 , train acc 0.853 ,test acc 0.832 \n",
      "epoch 90 ,loss 0.0002 , train acc 0.854 ,test acc 0.835 \n",
      "epoch 91 ,loss 0.0002 , train acc 0.854 ,test acc 0.834 \n",
      "epoch 92 ,loss 0.0002 , train acc 0.854 ,test acc 0.833 \n",
      "epoch 93 ,loss 0.0002 , train acc 0.854 ,test acc 0.834 \n",
      "epoch 94 ,loss 0.0002 , train acc 0.855 ,test acc 0.835 \n",
      "epoch 95 ,loss 0.0002 , train acc 0.855 ,test acc 0.834 \n",
      "epoch 96 ,loss 0.0002 , train acc 0.855 ,test acc 0.835 \n",
      "epoch 97 ,loss 0.0002 , train acc 0.855 ,test acc 0.833 \n",
      "epoch 98 ,loss 0.0002 , train acc 0.855 ,test acc 0.834 \n",
      "epoch 99 ,loss 0.0002 , train acc 0.855 ,test acc 0.833 \n",
      "epoch 100 ,loss 0.0002 , train acc 0.855 ,test acc 0.835 \n"
     ]
    }
   ],
   "source": [
    "loss=nn.CrossEntropyLoss()\n",
    "optimizer=torch.optim.SGD(net.parameters(),lr=0.1)\n",
    "\n",
    "def train_ch3(net,train_iter,test_iter,loss, num_epochs,batch_size,params=None,lr=None,optimizer=None):\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum,train_acc_sum,n=0.0,0.0,0\n",
    "        for X,y in train_iter:\n",
    "            copy_X=X.clone().to(device)\n",
    "            copy_y=y.clone().to(device)\n",
    "            # print(copy_y)\n",
    "            # print(copy_X)\n",
    "            # print(copy_X.device)\n",
    "            y_hat=net(copy_X)\n",
    "            # break\n",
    "            l=loss(y_hat,copy_y).sum()\n",
    "            if optimizer is not None:\n",
    "                optimizer.zero_grad()\n",
    "            elif params is not None and params[0].grad is not None:\n",
    "                for param in params :\n",
    "                    param.grad.data.zero_()\n",
    "            l.backward()\n",
    "            if optimizer is None:\n",
    "                sgd(params,lr,batch_size)\n",
    "            else :\n",
    "                optimizer.step()\n",
    "            train_l_sum+=l.item()\n",
    "            train_acc_sum+= (y_hat.argmax(dim=1)==copy_y).sum().item()\n",
    "            n+=copy_y.shape[0]\n",
    "        test_acc=evaluate_accuracy(test_iter,net)\n",
    "        print('epoch %d ,loss %.4f , train acc %.3f ,test acc %.3f '% (epoch +1,train_l_sum/n,train_acc_sum/n,test_acc))\n",
    "num_epochs=100\n",
    "train_ch3(net,train_iter,test_iter,loss,num_epochs,batch_size,None,None,optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义画图函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def use_svg_display():\n",
    "    display.set_matplotlib_formats('svg')\n",
    "def show_fashion_mnist(images,labels):\n",
    "    use_svg_display()\n",
    "    _,figs=plt.subplots(1,len(images),figsize=(12,12))\n",
    "    for f,img,lbl in zip(figs,images,labels):\n",
    "        f.imshow(img.view((28,28)).numpy())\n",
    "        f.set_title(lbl)\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试一下模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Created with matplotlib (https://matplotlib.org/) -->\n",
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       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2021-10-26T20:16:09.677729</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <cc:Agent>\n",
       "      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n",
       "     </cc:Agent>\n",
       "    </dc:creator>\n",
       "   </cc:Work>\n",
       "  </rdf:RDF>\n",
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       " <defs>\n",
       "  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\n",
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       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
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       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
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       "   </g>\n",
       "   <g clip-path=\"url(#p74f7e7d40c)\">\n",
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      ],
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X,y=iter(test_iter).next()\n",
    "true_labels=get_fashion_mnist_labels(y)\n",
    "net.cpu()\n",
    "pred_labels=get_fashion_mnist_labels(net(X).argmax(dim=1).numpy())\n",
    "titles=[true+'\\n'+pred for true,pred in zip(true_labels,pred_labels)]\n",
    "show_fashion_mnist(X[0:9],titles[0:9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分析\n",
    "\n",
    "可以看到实际上加入了噪声的数据没有影响训练结果，经过分析我认为是数据量不够，没有达到过拟合状态，模型没有训练到把这个噪声当作特征的阶段"
   ]
  }
 ],
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